Spatial analytical methods for deriving a historical map of physiological equivalent temperature of Hong Kong

Lai P-C, Choi CCY, Wong PPY, et al. Spatial analytical methods for deriving a historical map of physiological equivalent temperature of Hong Kong. Building and Environment. 2015;99:22-28

Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong.

BACKGROUND: Health authorities worldwide, especially in the Asia Pacific region, are seeking effective public-health interventions in the continuing epidemic of severe acute respiratory syndrome (SARS). We assessed the epidemiology of SARS in Hong Kong. METHODS: We included 1425 cases reported up to April 28, 2003. An integrated database was constructed from several sources containing information on epidemiological, demographic, and clinical variables. We estimated the key epidemiological distributions: infection to onset, onset to admission, admission to death, and admission to discharge. We measured associations between the estimated case fatality rate and patients' age and the time from onset to admission. FINDINGS: After the initial phase of exponential growth, the rate of confirmed cases fell to less than 20 per day by April 28. Public-health interventions included encouragement to report to hospital rapidly after the onset of clinical symptoms, contact tracing for confirmed and suspected cases, and quarantining, monitoring, and restricting the travel of contacts. The mean incubation period of the disease is estimated to be 6.4 days (95% CI 5.2-7.7). The mean time from onset of clinical symptoms to admission to hospital varied between 3 and 5 days, with longer times earlier in the epidemic. The estimated case fatality rate was 13.2% (9.8-16.8) for patients younger than 60 years and 43.3% (35.2-52.4) for patients aged 60 years or older assuming a parametric gamma distribution. A non-parametric method yielded estimates of 6.8% (4.0-9.6) and 55.0% (45.3-64.7), respectively. Case clusters have played an important part in the course of the epidemic. INTERPRETATION: Patients' age was strongly associated with outcome. The time between onset of symptoms and admission to hospital did not alter outcome, but shorter intervals will be important to the wider population by restricting the infectious period before patients are placed in quarantine

Modification by Influenza on Health Effects of Air Pollution in Hong Kong

Background: Both influenza viruses and air pollutants have been well documented as major hazards to human health, but few epidemiologic studies have assessed effect modification of influenza on health effects of ambient air pollutants. Objectives: We aimed to assess modifying effects of influenza on health effects of ambient air pollutants. Methods: We applied Poisson regression to daily numbers of hospitalizations and mortality to develop core models after adjustment for potential time-varying confounding variables. We assessed modification of influenza by adding variables for concentrations of single ambient air pollutants and proportions of influenza-positive specimens (influenza intensity) and their cross-product terms. Results: We found significant effect modification of influenza (p < 0.05) for effects of ozone. When influenza intensity is assumed to increase from 0% to 10%, the excess risks per 10-μg/m 3 increase in concentration of O 3 increased 0.24% and 0.40% for hospitalization of respiratory disease in the all-ages group and ≥ 65 year age group, respectively; 0.46% for hospitalization of acute respiratory disease in the all-ages group; and 0.40% for hospitalization of chronic obstructive pulmonary disease in the ≥ 65 group. The estimated increases in the excess risks for mortality of respiratory disease and chronic obstructive pulmonary disease in the all-ages group were 0.59% and 1.05%, respectively. We found no significant modification of influenza on effects of other pollutants in most disease outcomes under study. Conclusions: Influenza activity could be an effect modifier for the health effects of air pollutants particularly for O 3 and should be considered in the studies for short-term effects of air pollutants on health.published_or_final_versio

The Effects of Air Pollution on Mortality in Socially Deprived Urban Areas in Hong Kong, China

Background: Poverty is a major determinant of population health, but little is known about its role in modifying air pollution effects. Objectives: We set out to examine whether people residing in socially deprived communities are at higher mortality risk from ambient air pollution. Methods: This study included 209 tertiary planning units (TPUs), the smallest units for town planning in the Special Administrative Region of Hong Kong, China. The socioeconomic status of each TPU was measured by a social deprivation index (SDI) derived from the proportions of the population with a) unemployment, b) monthly household income < US$250, c) no schooling at all, d) one-person household, e) never-married status, and f) subtenancy, from the 2001 Population Census. TPUs were classified into three levels of SDI: low, middle, and high. We performed time-series analysis with Poisson regression to examine the association between changes in daily concentrations of ambient air pollution and daily number of deaths in each SDI group for the period from January 1996 to December 2002. We evaluated the differences in pollution effects between different SDI groups using a case-only approach with logistic regression. Results: We found significant associations of nitrogen dioxide, sulfur dioxide, particulate matter with aerodynamic diameter < 10 μm, and ozone with all nonaccidental and cardiovascular mortality in areas of middle or high SDI (p < 0.05). Health outcomes, measured as all nonaccidental, cardiovascular, and respiratory mortality, in people residing in high SDI areas were more strongly associated with SO 2 and NO 2 compared with those in middle or low SDI areas. Conclusions: Neighborhood socioeconomic deprivation increases mortality risks associated with air pollution.published_or_final_versio

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations

Quantifying the burden of disease due to premature mortality in Hong Kong using standard expected years of life lost

Plaß D, Chau PY, Thach T, et al. Quantifying the burden of disease due to premature mortality in Hong Kong using standard expected years of life lost. BMC Public Health. 2013;13(1): 863.Background To complement available information on mortality in a population Standard Expected Years of Life Lost (SEYLL), an indicator of premature mortality, is increasingly used to calculate the mortality-associated disease burden. SEYLL consider the age at death and therefore allow a more accurate view on mortality patterns as compared to routinely used measures (e.g. death counts). This study provides a comprehensive assessment of disease and injury SEYLL for Hong Kong in 2010. Methods To estimate the SEYLL, life-expectancy at birth was set according to the 2004 Global Burden of Disease study at 82.5 and 80 years for females and males, respectively. Cause of death data for 2010 were corrected for misclassification of cardiovascular and cancer causes. In addition to the baseline estimates, scenario analyses were performed using alternative assumptions on life-expectancy (Hong Kong standard life-expectancy), time-discounting and age-weighting. To estimate a trend of premature mortality a time-series analysis from 2001 to 2010 was conducted. Results In 2010 524,706.5 years were lost due to premature death in Hong Kong with 58.3% of the SEYLL attributable to male deaths. The three overall leading single causes of SEYLL were “trachea, bronchus and lung cancers”, “ischaemic heart disease” and “lower respiratory infections” together accounting for about 29% of the overall SEYLL. Further, self-inflicted injuries (5.6%; ranked 5) and liver cancer (4.9%; ranked 7) were identified as important causes not adequately captured by classical mortality measures. Scenario analyses highlighted that by using a 3% time-discount rate and non-uniform age-weights the SEYLL dropped by 51.6%. Using Hong Kong’s standard life-expectancy values resulted in an overall increase of SEYLL by 10.8% as compared to the baseline SEYLL. Time-series analysis indicates an overall increase of SEYLL by 6.4%. In particular, group I (communicable, maternal, perinatal and nutritional) conditions showed highest increases with SEYLL-rates per 100,000 in 2010 being 1.4 times higher than 2001. Conclusions The study stresses the mortality impact of diseases and injuries that occur in earlier stages of life and thus presents the SEYLL measure as a more sensitive indicator compared to classical mortality indicators. SEYLL provide useful additional information and supplement available death statistics

Improved estimation of functional enrichment in SNP heritability using feasible generalized least squares

Summary: Functional enrichment results typically implicate tissue or cell-type-specific biological pathways in disease pathogenesis and as therapeutic targets. We propose generalized linkage disequilibrium score regression (g-LDSC) that requires only genome-wide association studies (GWASs) summary-level data to estimate functional enrichment. The method adopts the same assumptions and regression model formulation as stratified linkage disequilibrium score regression (s-LDSC). Although s-LDSC only partially uses LD information, our method uses the whole LD matrix, which accounts for possible correlated error structure via a feasible generalized least-squares estimation. We demonstrate through simulation studies under various scenarios that g-LDSC provides more precise estimates of functional enrichment than s-LDSC, regardless of model misspecification. In an application to GWAS summary statistics of 15 traits from the UK Biobank, estimates of functional enrichment using g-LDSC were lower and more realistic than those obtained from s-LDSC. In addition, g-LDSC detected more significantly enriched functional annotations among 24 functional annotations for the 15 traits than s-LDSC (118 vs. 51)

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

BackgroundDepression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. ObjectiveThe aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. MethodsThis was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. ResultsA total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. ConclusionsDigital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk

Prevalence of psychological distress and its association with perceived indoor environmental quality and workplace factors in under and aboveground workplaces

Developing underground spaces serves a range of common urban functions, including workspaces. However, underground workplaces, work-related factors and the indoor environmental quality (IEQ) parameters within them may negatively affect worker’s mental health. This study assessed the prevalence of psychological distress with repeated measures over time in aboveground and underground workspaces, and assessed the association between perceived IEQ parameters and work-related factors with psychological distress. A total of 329 workers in similar aboveground and underground workspaces were followed-up in three assessments over 12 months in Singapore. Psychological distress was assessed using the 12-item General Health Questionnaire (GHQ-12) and defined as a GHQ-12 score ≥2. Perceived IEQ (air quality, temperature, noise, light) in the workplace were collected using the OFFICAIR questionnaire. We used generalised estimating equation models to assess the association between working underground, perceived IEQ, and work-related factors with psychological distress. The overall prevalence of psychological distress was 21.9%, 26.1% and 21.9%, at baseline, 3- and 12-months follow-up, respectively. The fully-adjusted multivariable analysis did not show any association between working underground and psychological distress however, perceived IEQ parameters and longer working hours were significantly associated with psychological distress. Regardless of working in under or aboveground workplaces, perceived IEQ was associated with psychological distress. Future studies are needed in order to examine the relationship between objective measures of IEQ and psychological distress and the impact of healthy building policies and improved IEQ on psychological distress